I am working with my CNN model right now and currently fine-tuning it. I choose nn.CrossEntropyLoss() as my loss function due to its strong ability to deal with multiple-class classification tasks. I read the loss function description and see the CrossEntropyLoss() has the following parameters: torch.nn.CrossEntropyLoss(weight=None, size_average=None, ignore_index=-100, reduce=None, reduction=‘mean’).
I was wondering that is there any way to optimize these parameters inside of nn.CrossEntropyLoss() function? Further, could I optimize any other loss functions?